Data Analysis
Mingye
2024-12-03
# data import
rat_df = read.csv("data/rat_2020_2024.csv")
food_scrap_df =
read.csv("data/food_scrap_drop_off.csv")
zhvi_df = read.csv("data/ny_zhvi_df_2020_2024.csv")
zori_df = read.csv("data/ny_zori_df_2020_2024.csv")
nyc_boroughs = st_read("Borough Boundaries.geojson")## Reading layer `Borough Boundaries' from data source
## `/Users/williamshie/Documents/Data Science/Final/final_2/Borough Boundaries.geojson'
## using driver `GeoJSON'
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS: WGS 84head(rat_df)## inspection_type job_ticket_or_work_order_id job_id job_progress bbl
## 1 Initial 13659068 PC8078519 1 4023290019
## 2 Initial 13361613 PC7824966 1 3072810182
## 3 Initial 13599220 PC8056345 1 2033950047
## 4 BAIT 2941952 PC8141304 5 4104480015
## 5 Initial 13788881 PC8254435 1 2057930467
## 6 Compliance 13383745 PC7780758 2 3034140068
## boro_code block lot house_number street_name zip_code x_coord y_coord
## 1 4 2329 19 48-03 59 STREET 11377 0 0
## 2 3 7281 182 145 SEABREEZE AVENUE 11224 0 0
## 3 2 3395 47 530 EAST 234 STREET 10470 0 0
## 4 4 10448 15 192-21 JAMAICA AVENUE 11423 1048743 199167
## 5 2 5793 467 3555 OXFORD AVENUE 10463 1009450 262094
## 6 3 3414 68 9 COVERT STREET 11207 0 0
## latitude longitude borough inspection_date result
## 1 40.73837 -73.90647 Queens 06/05/2023 03:19:22 PM Passed
## 2 40.57565 -73.97020 Brooklyn 04/05/2022 03:50:52 PM Passed
## 3 40.89732 -73.86322 Bronx 03/14/2023 08:40:50 AM Failed for Other R
## 4 40.71310 -73.76736 Queens 12/26/2023 12:16:51 PM Bait applied
## 5 40.88602 -73.90886 Bronx 01/23/2024 04:10:00 PM Rat Activity
## 6 40.68518 -73.91342 Brooklyn 05/09/2022 03:35:15 PM Passed
## approved_date location community_board
## 1 06/08/2023 05:06:47 PM 2
## 2 04/08/2022 03:54:46 PM 13
## 3 03/17/2023 02:48:23 PM 12
## 4 12/27/2023 10:10:53 AM (40.713101329334, -73.767362471917) 12
## 5 01/24/2024 11:33:03 AM (40.886019879068, -73.908861936068) 8
## 6 05/12/2022 08:19:56 AM 4
## council_district census_tract bin nta year month
## 1 26 245 4053773 Woodside 2023 6
## 2 48 35602 3196597 Coney Island-Sea Gate 2022 4
## 3 11 45101 2019758 Wakefield-Woodlawn 2023 3
## 4 23 482 4222146 Hollis 2023 12
## 5 11 295 2084152 Riverdale-Spuyten Duyvil 2024 1
## 6 37 401 3078992 Bushwick (East) 2022 5head(food_scrap_df)## borough nta_name
## 1 Brooklyn Bay Ridge
## 2 Manhattan East Midtown-Turtle Bay
## 3 Manhattan Hell's Kitchen
## 4 Manhattan East Midtown-Turtle Bay
## 5 Manhattan Tribeca-Civic Center
## 6 Staten Island St. George-New Brighton
## site_name
## 1 4th Avenue Presbyterian Church
## 2 Dag Hammarskjold Plaza Greenmarket
## 3 Hudson River Park's Pier 84 at W. 44th St.
## 4 58th Street Library FSDO
## 5 Tribeca Greenmarket
## 6 St. George Greenmarket
## site_addr
## 1 6753 4th Avenue, Brooklyn, NY 11220
## 2 E 47th St & 2nd Ave
## 3 Pier 84 at W. 44th St. near dog park
## 4 127 East 58th Street
## 5 Greenwich St. & Duane St
## 6 St. Marks Pl and Hyatt St
## hosted_by
## 1 4th Avenue Presbyterian Church
## 2 GrowNYC
## 3 Staff at Hudson River Park
## 4 GrowNYC
## 5 GrowNYC
## 6 NYC Compost Project Hosted by Snug Harbor Cultural Center & Botanical Garden
## open_month day_hours
## 1 Year Round Every day (Start Time: Dawn - End Time: Dusk)
## 2 Year Round Wednesday (Start Time: 8:00 AM - End Time: 12:30 PM)
## 3 Year Round Every day (Start Time: 7:00 AM - End Time: 7:00 PM)
## 4 Year Round Wednesdays (Start Time: 7:30 AM - End Time: 1:30 PM)
## 5 Year Round Saturday (Start Time: 8:00 AM - End Time: 1:00 PM)
## 6 Year Round Saturday (Start Time: 8:00 AM - End Time: 1:00 PM)
## notes
## 1 No meat, bones, or dairy.
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## website
## 1 <NA>
## 2 grownyc.org/compost
## 3 https://hudsonriverpark.org/the-park/sustainability/community-compost-program/
## 4 grownyc.org/compost
## 5 grownyc.org/compost
## 6 snug-harbor.org
## boro_cd council_dis ct2010 bbl bin latitude longitude police_prec object_id
## 1 310 47 3012600 NA NA 40.63551 -74.02277 68 31263
## 2 106 4 1009000 NA NA 40.75261 -73.96904 17 31123
## 3 104 3 1012901 NA NA 40.76346 -74.00025 18 31091
## 4 105 4 1011203 NA NA 40.76198 -73.96930 18 31084
## 5 101 1 1003900 NA NA 40.71742 -74.01079 1 30996
## 6 501 49 5000300 NA NA 40.64197 -74.07793 120 30956
## location_point app_android app_i_os assembly_district
## 1 POINT (-74.022767 40.635514) <NA> <NA> 51
## 2 POINT (-73.969036 40.752606) <NA> <NA> 74
## 3 POINT (-74.00025 40.76346) <NA> <NA> 67
## 4 POINT (-73.9693 40.76198) <NA> <NA> 73
## 5 POINT (-74.010793 40.717424) <NA> <NA> 66
## 6 POINT (-74.077928 40.641967) <NA> <NA> 61
## congress_district dsny_district dsny_section dsny_zone senate_district
## 1 10 BKS10 BKS101 BKS 17
## 2 12 MN06 MN063 MN 28
## 3 12 MN04 MN043 MN 47
## 4 12 MN05 MN052 MN 28
## 5 10 MN01 MN013 MN 27
## 6 11 SI01 SI012 SI 23head(zhvi_df)## RegionID SizeRank RegionName RegionType StateName State City
## 1 62080 4 11368 zip NY NY New York
## 2 62093 7 11385 zip NY NY New York
## 3 62019 9 11208 zip NY NY New York
## 4 62046 16 11236 zip NY NY New York
## 5 61807 17 10467 zip NY NY New York
## 6 62085 18 11373 zip NY NY New York
## Metro CountyName X2020.1.31 X2020.2.29
## 1 New York-Newark-Jersey City, NY-NJ-PA Queens County 528192.6 527257.9
## 2 New York-Newark-Jersey City, NY-NJ-PA Queens County 766285.8 766877.9
## 3 New York-Newark-Jersey City, NY-NJ-PA Kings County 584069.0 584128.8
## 4 New York-Newark-Jersey City, NY-NJ-PA Kings County 556447.0 558779.1
## 5 New York-Newark-Jersey City, NY-NJ-PA Bronx County 431082.0 426699.4
## 6 New York-Newark-Jersey City, NY-NJ-PA Queens County 675125.7 675670.1
## X2020.3.31 X2020.4.30 X2020.5.31 X2020.6.30 X2020.7.31 X2020.8.31 X2020.9.30
## 1 524266.9 522275.8 521385.9 520385.5 516688.1 510385.4 504947.1
## 2 767261.6 768287.1 768536.4 767880.3 766388.6 764911.0 763249.7
## 3 585511.1 588232.9 591933.8 592971.8 591628.3 589038.7 587388.2
## 4 561201.6 563977.4 566000.7 565868.6 564319.2 562457.2 561660.1
## 5 417166.3 409128.2 404095.7 402165.0 402087.6 401990.7 405016.7
## 6 673500.0 668760.7 664276.7 662083.9 654935.9 644841.3 635070.8
## X2020.10.31 X2020.11.30 X2020.12.31 X2021.1.31 X2021.2.28 X2021.3.31
## 1 503106.5 503648.4 506265.0 508977.3 512132.8 514154.6
## 2 759656.1 756066.8 753333.5 752381.3 753657.6 756193.4
## 3 586779.6 587397.1 588714.3 590735.8 593778.1 597376.2
## 4 561378.6 562520.3 564734.6 568278.6 572763.3 578465.5
## 5 408095.2 412207.6 416081.1 421889.1 431480.8 444899.4
## 6 630178.7 626333.1 623006.2 618409.3 617337.5 618486.7
## X2021.4.30 X2021.5.31 X2021.6.30 X2021.7.31 X2021.8.31 X2021.9.30 X2021.10.31
## 1 513545.6 513274.3 512169.7 513647.7 515205.9 517516.9 520167.9
## 2 760293.4 764712.1 768415.4 771259.6 772388.5 773570.8 775890.5
## 3 602246.5 606895.0 609155.7 610491.7 610604.1 613045.0 617421.0
## 4 584991.4 591055.9 594904.9 596912.1 597603.3 597795.9 598878.2
## 5 455437.9 462904.9 461875.1 453605.1 438793.4 421945.6 411599.9
## 6 619560.2 620293.7 617666.3 618197.6 618781.9 619884.8 620588.8
## X2021.11.30 X2021.12.31 X2022.1.31 X2022.2.28 X2022.3.31 X2022.4.30
## 1 521725.6 521886.5 519710.8 517534.2 516469.2 516859.7
## 2 779431.8 782268.2 783578.9 783441.1 785378.3 788904.9
## 3 623356.3 628199.3 630765.7 631970.0 633731.6 636190.4
## 4 601476.8 604396.6 607793.6 611521.1 616361.0 620093.7
## 5 403827.1 397758.7 394423.8 398191.9 407522.6 413924.8
## 6 619487.9 617252.4 614656.6 614966.0 617660.6 619955.0
## X2022.5.31 X2022.6.30 X2022.7.31 X2022.8.31 X2022.9.30 X2022.10.31
## 1 516227.9 516353.1 516371.5 514825.7 511602.0 508097.2
## 2 796084.2 804432.2 810063.0 811312.9 807884.0 804876.2
## 3 640465.9 645628.6 649119.5 649810.3 647188.8 644657.8
## 4 622656.0 625384.0 627041.1 627201.2 623448.9 620023.4
## 5 414126.5 412279.0 408672.8 402399.6 392358.9 384807.9
## 6 619968.6 621096.5 619121.3 612323.0 601194.8 591469.8
## X2022.11.30 X2022.12.31 X2023.1.31 X2023.2.28 X2023.3.31 X2023.4.30
## 1 506580.5 503289.1 497724.8 490815.4 484453.6 477787.1
## 2 802838.2 802151.0 800703.4 798950.1 797761.4 799366.6
## 3 642264.1 641399.5 640105.2 637786.4 636126.6 640210.0
## 4 616886.2 614528.1 610691.6 606678.5 604931.1 608805.4
## 5 380556.4 379171.4 374879.9 373540.1 375699.4 384275.2
## 6 584564.3 577927.4 569925.2 564723.3 561582.7 556310.6
## X2023.5.31 X2023.6.30 X2023.7.31 X2023.8.31 X2023.9.30 X2023.10.31
## 1 470639.5 465210.9 463377.3 463371.3 462810.6 460771.9
## 2 801223.4 802036.8 801422.6 800283.2 798291.5 797014.0
## 3 648346.8 654695.9 657573.2 657020.5 656315.8 655178.3
## 4 615239.9 621151.8 624614.7 626492.5 627861.0 628682.6
## 5 391838.2 396577.9 399856.9 402236.0 403255.3 400097.0
## 6 545838.3 531434.2 521217.5 513782.0 509985.5 505959.8
## X2023.11.30 X2023.12.31 X2024.1.31 X2024.2.29 X2024.3.31 X2024.4.30
## 1 457397.8 453086.2 450240.7 448627.2 451705.6 456243.1
## 2 795779.1 795640.7 794282.4 794342.2 797380.4 802193.9
## 3 654648.3 656263.8 658709.2 661959.5 666440.0 670735.0
## 4 629011.8 630674.1 632713.9 635540.9 638623.8 641376.0
## 5 394496.2 386985.9 382000.0 378034.4 377532.6 374809.3
## 6 501444.1 497843.2 493967.4 492163.3 491429.0 492810.0
## X2024.5.31 X2024.6.30 X2024.7.31 X2024.8.31 X2024.9.30 X2024.10.31
## 1 460893.5 461600.4 461088.3 460451.8 461417.2 461167.7
## 2 805219.7 804711.7 804494.0 806317.1 810315.1 814334.5
## 3 673816.0 674124.7 674583.6 675525.6 677539.8 678312.6
## 4 642619.7 641839.5 641629.1 643184.2 646821.1 650637.9
## 5 372827.7 369063.0 368696.4 369597.6 371425.2 371800.4
## 6 495404.8 497218.9 499719.7 501979.1 503272.5 505398.0head(zori_df)## RegionID SizeRank RegionName RegionType StateName State City
## 1 62080 4 11368 zip NY NY New York
## 2 62093 7 11385 zip NY NY New York
## 3 62019 9 11208 zip NY NY New York
## 4 62046 16 11236 zip NY NY New York
## 5 61807 17 10467 zip NY NY New York
## 6 62085 18 11373 zip NY NY New York
## Metro CountyName X2020.1.31 X2020.2.29
## 1 New York-Newark-Jersey City, NY-NJ-PA Queens County NA NA
## 2 New York-Newark-Jersey City, NY-NJ-PA Queens County 2448.553 2456.115
## 3 New York-Newark-Jersey City, NY-NJ-PA Kings County NA NA
## 4 New York-Newark-Jersey City, NY-NJ-PA Kings County NA NA
## 5 New York-Newark-Jersey City, NY-NJ-PA Bronx County 1701.668 1720.197
## 6 New York-Newark-Jersey City, NY-NJ-PA Queens County NA NA
## X2020.3.31 X2020.4.30 X2020.5.31 X2020.6.30 X2020.7.31 X2020.8.31 X2020.9.30
## 1 NA NA NA NA NA NA NA
## 2 2441.699 2414.372 2414.585 2408.849 2416.578 2369.325 2343.459
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 1722.498 1702.781 1658.133 1644.647 1707.506 1753.169 1799.189
## 6 NA NA NA NA NA NA NA
## X2020.10.31 X2020.11.30 X2020.12.31 X2021.1.31 X2021.2.28 X2021.3.31
## 1 NA NA NA NA NA NA
## 2 2311.269 2277.187 2248.553 2233.961 2224.142 2225.220
## 3 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA
## 5 1775.834 1826.802 1776.000 1796.651 1753.189 1789.808
## 6 NA NA NA NA NA NA
## X2021.4.30 X2021.5.31 X2021.6.30 X2021.7.31 X2021.8.31 X2021.9.30 X2021.10.31
## 1 NA NA NA NA NA NA NA
## 2 2223.716 2263.851 2310.595 2361.465 2391.628 2421.895 2458.039
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 1741.302 1766.672 1775.301 1807.147 1802.181 1833.622 1871.474
## 6 1894.899 1906.100 1990.802 2012.177 2031.377 2045.573 1997.025
## X2021.11.30 X2021.12.31 X2022.1.31 X2022.2.28 X2022.3.31 X2022.4.30
## 1 NA NA NA NA NA NA
## 2 2493.205 2522.206 2537.980 2538.440 2549.701 2592.160
## 3 NA NA NA NA 2285.018 2303.287
## 4 NA NA NA NA NA NA
## 5 1870.410 1799.790 1772.979 1830.934 1881.293 1931.986
## 6 2020.485 2008.840 2007.273 2050.381 2088.892 2164.162
## X2022.5.31 X2022.6.30 X2022.7.31 X2022.8.31 X2022.9.30 X2022.10.31
## 1 NA NA NA NA NA NA
## 2 2665.379 2764.065 2823.873 2873.828 2878.283 2887.580
## 3 2362.293 2379.109 2352.997 2367.711 2348.447 2408.106
## 4 2226.912 2282.301 2265.703 2310.780 2305.516 2432.515
## 5 1924.659 1948.381 1985.830 1992.896 1981.437 1944.423
## 6 2170.632 2232.924 2264.414 2251.311 2230.028 2227.687
## X2022.11.30 X2022.12.31 X2023.1.31 X2023.2.28 X2023.3.31 X2023.4.30
## 1 NA NA NA NA NA NA
## 2 2860.363 2825.818 2787.999 2785.287 2797.609 2833.309
## 3 2436.682 2481.746 2502.737 2501.262 2493.180 2514.730
## 4 2415.473 2499.901 2345.235 2336.725 2292.325 2355.621
## 5 1944.814 1951.174 1987.002 2002.966 2018.190 1964.398
## 6 2250.507 2255.664 2288.122 2298.252 2300.585 2296.878
## X2023.5.31 X2023.6.30 X2023.7.31 X2023.8.31 X2023.9.30 X2023.10.31
## 1 NA NA NA NA NA NA
## 2 2875.260 2914.789 2929.464 2938.923 2952.872 2954.371
## 3 2533.124 2596.065 2575.544 2558.233 2550.770 2590.156
## 4 2415.095 2506.227 2545.207 2531.421 2575.470 2566.624
## 5 2017.347 2094.580 2121.534 2136.091 2214.768 2275.591
## 6 2295.696 2331.748 2350.530 2391.866 2376.860 2397.750
## X2023.11.30 X2023.12.31 X2024.1.31 X2024.2.29 X2024.3.31 X2024.4.30
## 1 NA NA NA NA NA NA
## 2 2938.652 2905.363 2900.733 2897.087 2948.450 2958.387
## 3 2695.514 2745.312 2781.866 2760.926 2739.610 2679.047
## 4 2655.016 2670.999 2766.037 2809.330 2878.693 2825.073
## 5 2348.462 2280.554 2321.299 2290.718 2304.462 2342.780
## 6 2384.238 2373.139 2372.172 2362.518 2372.199 2392.090
## X2024.5.31 X2024.6.30 X2024.7.31 X2024.8.31 X2024.9.30 X2024.10.31
## 1 NA NA 2495.950 2451.786 2696.151 2627.583
## 2 3024.869 3054.979 3123.694 3121.569 3085.313 3037.952
## 3 2606.380 2638.616 2594.905 2669.834 2700.378 2966.500
## 4 2853.657 2756.713 2775.097 2882.028 2992.892 2998.333
## 5 2317.591 2345.020 2332.908 2306.385 2200.925 2252.289
## 6 2470.749 2536.356 2588.668 2568.784 2596.982 2514.722# Filter for rows where the result indicates rodent activity
data_rodent <- rat_df %>% filter(grepl("Rat Activity", result, ignore.case = TRUE))
# Group by neighborhood (NTA), year, and month to count occurrences of rodent activity
rodent_activity_summary <- data_rodent %>%
group_by(nta, year, month) %>%
summarise(count = n(), .groups = 'drop') %>%
arrange(desc(count))
# Print the top neighborhoods with the highest rodent activity
print(head(rodent_activity_summary))## # A tibble: 6 × 4
## nta year month count
## <chr> <int> <int> <int>
## 1 Harlem (South) 2022 3 92
## 2 Crown Heights (North) 2022 3 89
## 3 Bedford-Stuyvesant (West) 2021 12 83
## 4 Harlem (South) 2022 8 80
## 5 Sunset Park (West) 2024 5 78
## 6 Flatbush 2024 8 77# Filter data for the year 2020
rodent_activity_2020 <- rodent_activity_summary %>% filter(year == 2020)
# Create a bar plot for rodent activity in 2020
ggplot(data = rodent_activity_2020, aes(x = month, y = count)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Rodent Activity in 2020",
x = "month",
y = "Rodent Activity Count") 
Purpose:
The bar plot shows how rodent activity varies across months in 2020. This helps identify seasonal trends, such as higher rodent activity in summer months. Findings:
There is a clear peak in rodent activity during the summer months (July and August). A significant drop in activity is observed in the spring (April). Justification:
Understanding monthly trends in rodent activity can inform targeted pest control strategies during peak seasons.
# Aggregate data by year and month
yearly_monthly_summary <- data_rodent %>%
group_by(year, month) %>%
summarise(total_activity = n(), .groups = 'drop') %>%
arrange(year, month)
# Create a line plot showing rodent activity over time
ggplot(yearly_monthly_summary, aes(x = as.numeric(month), y = total_activity, color = as.factor(year), group = year)) +
geom_line(size = 1) +
geom_point() +
scale_x_continuous(breaks = 1:12) +
labs(title = "Monthly Rodent Activity by Year (2020-2024)",
x = "Month",
y = "Total Rodent Activity Reports",
color = "Year") +
theme_minimal() +
theme(legend.position = "right")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Objective: To identify patterns in rodent activity across months and
years. To explore temporal trends and assess year-over-year
consistency.
Findings: Rodent activity generally peaks during the summer months (June to September), regardless of the year. In some years, there are anomalies (e.g., dips in October for 2024). A gradual increase in activity is evident over the years, suggesting either more rodents or improved reporting mechanisms.
# Analyze the distribution of food scrap drop-off sites by borough
borough_distribution <- food_scrap_df %>%
group_by(borough) %>%
summarise(count = n()) %>%
arrange(desc(count))
# Display the borough distribution
print("Borough Distribution:")## [1] "Borough Distribution:"print(borough_distribution)## # A tibble: 5 × 2
## borough count
## <chr> <int>
## 1 Manhattan 251
## 2 Brooklyn 171
## 3 Bronx 75
## 4 Queens 75
## 5 Staten Island 19# Borough distribution plot
ggplot(borough_distribution, aes(x = reorder(borough, -count), y = count)) +
geom_bar(stat = "identity", fill = "skyblue") +
theme_minimal() +
labs(title = "Distribution of Food Scrap Drop-off Sites by Borough",
x = "Borough",
y = "Number of Sites") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Objective: Analyze the geographic distribution of food scrap drop-off
sites across NYC boroughs. Identify whether disparities exist in access
to food scrap sites between boroughs.
Findings: Manhattan has the highest number of drop-off sites, followed by Brooklyn. Bronx, Queens, and Staten Island have significantly fewer sites. The disparity suggests possible inequities in access to food scrap recycling programs, particularly for Staten Island residents.
# Analyze the temporal patterns
# First, let's create a simplified season categorization
food_scrap_df <- food_scrap_df %>%
mutate(
operation_type = case_when(
grepl("Year Round", open_month, ignore.case = TRUE) ~ "Year Round",
grepl("Seasonal|Spring|Summer|Fall|Winter", open_month, ignore.case = TRUE) ~ "Seasonal",
TRUE ~ "Specific Months"
)
)
# Create a summary of operational patterns
operation_summary <- food_scrap_df %>%
group_by(operation_type) %>%
summarise(count = n())
print("Operation Pattern Summary:")## [1] "Operation Pattern Summary:"print(operation_summary)## # A tibble: 3 × 2
## operation_type count
## <chr> <int>
## 1 Seasonal 5
## 2 Specific Months 27
## 3 Year Round 559# Create a scatter plot of locations
ggplot(food_scrap_df, aes(x = longitude, y = latitude)) +
geom_point(aes(color = borough), alpha = 0.6) +
theme_minimal() +
labs(title = "Geographic Distribution of Food Scrap Drop-off Sites",
x = "Longitude",
y = "Latitude") +
theme(legend.position = "right")
Objective: To analyze the spatial distribution of food scrap drop-off
sites across boroughs. To visualize clustering and disparities in
drop-off site availability.
Findings: Drop-off sites are densely clustered in Manhattan and Brooklyn. Sparse distribution in Staten Island reflects fewer food scrap programs in this borough. Queens and Bronx show moderate distribution but lack the density seen in Manhattan.
plot_county <- ggplot(zhvi_df, aes(x = CountyName, fill = CountyName)) +
geom_bar() +
labs(
title = "Demographic Differences by Borough",
x = "Borough",
y = "Count",
fill = "Borough"
) +
theme_minimal() +
theme(
axis.text.x = element_text(size = 8, angle = 90, hjust = 1, vjust = 0.5), # Rotate and resize x-axis text
plot.title = element_text(size = 16, face = "bold"), # Enlarge title
axis.title.x = element_text(size = 14), # Enlarge x-axis label
axis.title.y = element_text(size = 14), # Enlarge y-axis label
legend.position = "none" # Remove legend to reduce clutter
)
print(plot_county)
Objective: Analyze the geographic distribution of food scrap drop-off
sites across NYC boroughs. Identify whether disparities exist in access
to food scrap sites between boroughs. Findings: Manhattan has the
highest number of drop-off sites, followed by Brooklyn. Bronx, Queens,
and Staten Island have significantly fewer sites. The disparity suggests
possible inequities in access to food scrap recycling programs,
particularly for Staten Island residents.
# Filter rodent activity data for July
rodent_july <- data_rodent %>% filter(month == 8)
# Assuming food_scrap_df is available, filter it for July operations
food_scrap_july <- food_scrap_df %>% filter(grepl("July", open_month, ignore.case = TRUE))
# Create a scatter plot showing both rodent activity and food scrap sites in July
ggplot() +
geom_point(data = rodent_july, aes(x = longitude, y = latitude, color = "Rodent Activity"), alpha = 0.6) +
geom_point(data = food_scrap_july, aes(x = longitude, y = latitude, color = "Food Scrap Sites"), alpha = 0.6) +
theme_minimal() +
labs(title = "Interaction of Rodent Activity and Food Scrap Sites in July",
x = "Longitude",
y = "Latitude",
color = "Legend") +
theme(legend.position = "right")
# Load necessary libraries
library(leaflet)
# Filter rodent activity data for July
rodent_july <- data_rodent %>% filter(month == 8)
# Define NYC boroughs data (assuming nyc_boroughs is preloaded)
# Colors for boroughs
colors <- setNames(c("#FF0005", "#F0FDF0", "#5030FF", "#FFEF00", "#DF00F0"), unique(nyc_boroughs$boro_name))
# Create the leaflet map
rat_map <- leaflet() %>%
addProviderTiles(providers$OpenStreetMap, options = providerTileOptions(noWrap = TRUE)) %>%
setView(lng = -74.0060, lat = 40.7128, zoom = 11) %>%
addPolygons(data = nyc_boroughs,
fillColor = ~colors[as.character(nyc_boroughs$boro_name)],
fillOpacity = 0.5,
color = colors,
weight = 1,
popup = ~nyc_boroughs$boro_name) %>%
addCircleMarkers(data = rodent_july,
lng = ~longitude, lat = ~latitude,
radius = 3,
color = "#00F0FF",
fillOpacity = 0.5,
popup = paste("<strong>Borough: </strong>", rodent_july$borough,
"<br><strong>Date: </strong>", format(as.Date(paste0(rodent_july$year, "-", rodent_july$month, "-01")), "%Y-%m")))
# Print the map
rat_map## Input to asJSON(keep_vec_names=TRUE) is a named vector. In a future version of jsonlite, this option will not be supported, and named vectors will be translated into arrays instead of objects. If you want JSON object output, please use a named list instead. See ?toJSON.
## Input to asJSON(keep_vec_names=TRUE) is a named vector. In a future version of jsonlite, this option will not be supported, and named vectors will be translated into arrays instead of objects. If you want JSON object output, please use a named list instead. See ?toJSON.Objective: The map overlays rodent activity data and food scrap drop-off sites for July, a peak month for rodent activity. The visualization aims to understand the spatial relationship between these two factors.
Clustered Rodent Activity:
Rodent activity is heavily concentrated in high-density urban areas, particularly Manhattan and parts of Brooklyn. Sparse activity is observed in Staten Island, which aligns with its lower population density. Food Scrap Drop-off Sites:
The distribution of food scrap sites shows a notable overlap with high rodent activity areas, particularly in Manhattan and Brooklyn. Staten Island has both fewer rodent activity reports and fewer food scrap sites, indicating potential inequity in access to waste management resources.